In the competitive landscape of SaaS success, understanding your market is the only way to survive. The public service announcement “talk to customers” has emerged as a kind of mantra among founders and product leaders. This is fundamentally good advice, but because customer conversations provide only one set of information, leaning on them alone creates perilous blind spots in your SaaS research strategy.
Here’s why customer conversations are only a small part of a holistic SaaS research framework, and how broadening your methodologies may be the difference between market leader and market loser.
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Why do SaaS founders make such a big deal about talking to customers?
The mantra “talk to customers” was popularized by both the lean startup movement and the customer development frameworks. Its appeal is understandable:
It’s action-oriented, and makes the founders feel progressive, a unique momentum from the otherwise ambiguous act of the early stages of product development.” It offers immediate, concrete feedback that can confirm or contradict hypotheses in not much time at all, unlike market research documents that might take weeks to assemble. This cultivates a customer-centric approach that is consistent with contemporary product development philosophies, such as those advocated in “Inspired” by thought leader Marty Cagan. Plus, it’s relatively simple and inexpensive compared to other research methods — no specialized training or rent-an-expert tools are needed, just a calendar and active listening.
That no longer becomes good advice when it turns from, “talk to customers as part of doing the research” to, “all you need to do is talk to customers.” The truth is much more complicated than that.
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What are the limitations of relying solely on customer conversations?
Can your customers articulate their true needs?
Customers rarely articulate exactly what they need. The famous (perhaps apocryphal) quote attributed to Henry Ford comes to mind: “If I’d asked people what they wanted, they would have said faster horses.”
This is not because customers are trying to fool you. Rather, they:
Customers often don’t even understand their problems, as they suffer from cognitive biases that distort their view of their needs, a phenomenon documented in the field of behavioral economics, in the book “Thinking, Fast and Slow” by Daniel Kahneman. Humans are wired to use mental models based on previous experiences and therefore tend to find solutions within existing paradigms. For example, early SaaS email marketing customers would often ask for features that replicated their desktop software instead of exploring new cloud capabilities. Customers usually speak about their immediate pain points without realizing where the root cause may reside — like the way someone describes symptoms when speaking to a doctor, without really knowing what they should be asking for. Also, they rarely envision truly transformative solutions—as identified in the work of Clayton Christensen’s research on disruptive innovation; conventionally customers asked organizations to move forward incrementally, not to think holistically and ask for improvements in a different paradigm.
SaaS research involves digging deeper than what customers outright say to uncover what they truly need, even when they don’t know it yet.
How do you account for selection bias?
The customers who will speak with you are a biased sample. They typically:
Research-engaging customers tend to have a higher involvement than the per-user; usually, they are your power users who are using your product more frequently and deeply than the silent majority. Research by Nielsen Norman Group states that these vocal users will typically account for 0.5 to 1% of your entire user base. They tend to hold more extreme (positive or negative) opinions that compel them to leave feedback, which will likely color your perception toward radical opinions. These subjects generally have greater availability than busy executives or operational staff who may make up your real target market — but who are unable to find 60 minutes to talk. Also, they may be more technically capable than your average user. This raises the risk for products that are meant for non-tech audiences
This selection bias can give rise to product decisions that cater to the highly vocal minorities, but that may miss your broader user base. These biases have to be accounted for, and the only way to address them is through multi-faceted research methodologies.
So what do you say to your future customers?
One of the most obvious limitations of customer conversations is that they only involve customers and present. Which creates a major blind spot around:
Prospects who evaluated but rejected your solution provide potentially rich information about weaknesses in your competition, or positioning issues you may need to address. For example, when Slack did research with companies who picked Microsoft Teams instead, they found that concerns around pricing structure fed into future enterprise plans. The feedback that depends heavily on either generalizing or ignoring the potential enterprise market is blind to those market segments that you haven’t already penetrated: Dropbox didn’t notice that individual users existed until they did market research and realized a huge enterprise opportunity was waiting to be tapped outside of those early adopters. Users who abandoned their product without leaving feedback—which Mixpanel’s 2023 Product Benchmarks Report shows accounts for 90-97% of all churning users—take their criticisms to other places, typically to your rivals. Emerging segments, such as when Zoom discovered segments outside their existing customer base in telehealth with needs they weren’t vocalizing previously, are growth opportunities that customer-centric research can’t uncover.
To improve growth, SaaS research needs to go beyond your customers and look at the overall market.
How can you build a more robust SaaS research framework?
What quantitative methods should supplement your qualitative research?
It should also be supplemented by quantitative methods. Customer conversations are rich in qualitative data, but …
Usage analytics: Study the product usage to find insights beyond subjective feedback. At the same time, the features that drive the most engagement often come out in the data patterns and less in the customers’ articulation
for example, Hubspot discovered that users who create customized dashboards in their first week are 32% more likely to stay, but this never came up in a feedback session. The points of friction/abandonment reveal themselves starkly in funnel analytics, and Amplitude’s research indicates that users generally abandon processes after waiting over 3 seconds for a response or interacting with over 5 form fields. In analytics, a common source of such discrepancies is between what your customers claim they want and what they truly desire—one perennial example comes from a major CRM vendor whose customers consistently clamored for yet more reporting whilst logs told a different story, showing that usage of existing reports was marginal.
Market analysis: Look at data of the market as a whole to get a bigger picture than what customers provide through their opinions. Forecasts of total addressable market (TAM) size and growth from vendors like Gartner and Forrester provide critical reality checks on the actual potential of customer-requested functionality. Opportunities for market segmentation sometimes surface through demographic and firmographic data customers themselves would not articulate, such as when Salesforce developed industry-specific solutions based on market data rather than customer feedback. Gaps and opportunities that transcend current customer needs are revealed through competitive positioning analysis from companies such as G2 and TrustRadius. Pricing elasticity studies (most notably by Price Intelligently, now ProfitWell) have repeatedly revealed that 80% of SaaS companies are underpriced given the value they’re delivering—something that seldom comes to light in customer conversations.
A/B testing: Test hypotheses in a systematic way via controlled experiments to eliminate subjective bias. You will have no choice, for example, if some kind of feature had variations (with different implementations) that could be tested objectively, like when Canva found through product testing that people preferred more pre-made templates that allowed them to easily switch between them than an advanced customization tool that allows them to achieve better results, unlike what their feedback said during interviews. Messaging tests can yield surprising results like the one we had at Wistia where an audience of designers and developers preferred technical language over benefit-oriented copy despite customers reporting a preference for the latter. Data from pricing models tested in the market,
for example, HubSpot learned they only increased their conversion by 35% when they unbundled pricing from all-inclusive packages, despite focus groups with audiences around the world answering that they preferred all-inclusive packages. Often onboarding flows improved through experimentation fly in the face of user preference like Intercom’s finding that shorter, progressive onboarding boosted their activation by a massive 22% despite users indicating they wanted to “see everything at once”.
These quantitative methodologies offer the statistical validity that conversations often lack, resulting in a more balanced approach to SaaS research.
How can you expand beyond current customer insights?
To get beyond the constraints of only talking to existing customers:
Market research: Take customers to a higher level of understanding of the market dynamics beyond your customer base by analyzing competitors. In contrast to the clean and linear data used by these tools, alternative solutions to problems reveal different perspectives on customer needs — not least, Notion’s porous analysis of Evernote identified opportunities in collaborative note-taking that Evernote users never asked for. Competitive feature sets and pricing strategies deliver benchmarking data; one example: according to SaaS pricing strategist Patrick Campbell, companies that routinely assess their competitors’ pricing grow 30 percent faster than those that don’t.
Industry perspective: Help connect the dots over what broader industry trends will affect customer needs deeper than customers themselves can articulate. Changes in technology that impact your market, often precede customer needs by 12-24 months. Regulatory changes that impact customers — such as GDPR’s effect on data management practices — engender compliance needs and leads that wouldn’t arise in the typical customer conversation. Economic factors that affect buying decisions like the shift toward cost optimization in times of economic trouble, according to documentation by Gartner, impact purchasing priorities in ways that customers may not communicate explicitly. These are emerging business models, and, as the subscription economy trends in Zuora’s Subscription Economy Index show, they’re changing expectations in every industry long before they show up in customer demand.
Prospect interviews: Map non-customers and interview them to understand how the market thinks about your offering beyond your current customers. Interviews with people who evaluated your solution but didn’t buy it often yield the most useful feedback; Drift uncovered key pricing objections through lost prospect interviews that existing customers never mentioned. Follow your price-sensitive customers to see patterns of preference; Buffer discovered that flexibility of posting schedule was a bigger consideration than analytics capabilities by speaking to social media managers who chose competitors to their platform who met their ideal customer profile. It’s common for adjacent market segments to have similar needs, so they often provide one way to uncover expansion opportunities
This broader perspective helps ensure that your SaaS research captures insights that reach beyond your existing customer base, unlocking growth opportunities that you might otherwise overlook.
When should you employ observational research?
There is often a discrepancy between what customers think and what they do. Observational research methods are helping fill this gap:
Usability testing: Observe users using your product under controlled conditions to reveal insights that go unnoticed in conversation alone. Detect unreported usability problems, as proven in Jakob Nielsen’s foundational research that 60-80% of usability issues go unreported because users don’t complain about unrealistic requests but rather assume, “The problem must be mine,” (which was also similar to the case on the website here). Observe to find out expectations and mental models; Optimizely found through usability testing that marketers mentally conceived A/B tests spatially (as “versions”) vs. temporally(as “campaigns”), leading to the company dramatically reorganizing their interface without any customers ever directly asking them to do so. Identify workarounds and workarounds that expose product limitations–for example, when Figma noticed that a lot of users would create these elaborate component naming conventions to make up for organizational limits that were never addressed in feedback sessions.
Context inquiry: Watch your customers interact with your product in their actual work settings to learn about true usage contexts. Understand how your solution plugs into more holistic workflows that customers may never formulate in full; when Asana did workplace observations, they found users were looking at their task management tools and communication tools in parallel, which led them to build out integrated messaging functions. Determine integration points and sources of friction with other tools in the tech stack; Zapier’s field studies showed customers manually copied data between systems at specific organizational handoff points, which led to creating automated triggers to alleviate those eventualities. Researching Complementary Use Cases Discovery — Sometimes you realize new use cases for your product organically by working in a company, or even through interviews with your customers; Airtable identified that many of their customers are using their product as a CRM, despite the fact it wouldn’t be a traditional one and, as a consequence, pivoting the new product direction. Take into account all organizational constraints that impact usage (such as approval workflows or security requirements)
Session recordings: Identify user behavior at scale by reviewing recordings of actual product usage; this provides insight into unfiltered user behavior. Reveal common navigation patterns that indicate the way users intuitively try to navigate through your interface; FullStory’s aggregate path analysis usually gives you suggested navigational shortcuts that over 80% of users try (and are rarely mentioned in feedback) but that just don’t make it into verbal feedback. Observing people discover features helps to understand the challenges: Miro discovered through session recording analysis that some users were missing some key collaboration features that arise through a contextual onboarding experience, increasing their feature adoption by 48% Know the abandoned processes and triggers of persisting situations.
How do you integrate multiple research methodologies effectively?
What should your research timeline look like?
The best practice for finding a good SaaS is a timeline:
Ongoing background research:
- Ongoing competitor monitoring
- Analysis of industry trends regularly
- Market reports are to be reviewed periodically
Quarterly deep dives:
- In-depth analysis of usage data
- Creating structured customer interview programs
- Usability testing of key features
Project-specific research:
- Feature research to discover new functionality
- Pre-launch validation testing
- Post-launch impact assessment
This layered approach not only allows you to keep a finger on the pulse of the market but also affords your team independence on certain product initiatives.
How do you triangulate findings across methodologies?
Triangulation — comparing results across different research approaches — is what truly makes multi-method SaaS research powerful:
Recognize patterns and contradictions:
- Does what you see customers doing match what they say they are doing?
- Does customer expressed needs match with market data?
- Instead, are rivals solving problems that your customers haven’t even brought up?
Develop and test hypotheses:
- Hypothesize off of one methodology
- Cross-validate using alternative methods
- ‘Refine on contradictory evidence’
Be cautious when considering what evidence to weigh:
- Use statistics instead of anecdotes
- Assess methodological strengths and weaknesses
This triangulation process distills myriad disparate research activities into an integrated SaaS research strategy that instigates decisive decision-making.
What organizational structures support comprehensive SaaS research?
How do you build research capabilities within your team?
To build good SaaS research capabilities take:/p>
Skills development: Building research skills across your whole organization is a source of sustainable competitive advantage. Consider training product managers basic research methodologies through things like ProductFaculty’s Product Research Certification, which converts casual conversations into actual meaningful insights. Learning, practicing and implementing skills such as laddering questions and directed storytelling Note though that exploring interview skills beyond casual conversations can be found in the book Interviewing Users by respected researcher Steve Portigal. Develop analytics capacity throughout the organization by making sure team members can conduct basic cohort analysis and funnel tracking—Amplitude’s education team discovered that companies with broadly distributed analytics skills released features about 26 percent faster.
Tools investment: With the right tools, your research efforts are multiplied. Implement the right analytics-heavy tools, like Mixpanel, Amplitude, or Heap, to turn behavioral data into actionable business insights; according to Forrester’s 2023 SaaS Product Analytics Wave report, companies with mature analytics platforms realized 21% higher revenue growth than competitors. Join user research tools that suit your pocket, with enterprise tools such as UserTesting or more affordable tools like Maze or Lookback, which universalize research funding.
Culture shift: Cultural underpinnings are necessary to support sustainable research practices. Transition from opinionated decisions to evidence-based decisions where you define simple thresholds upfront to help identify when research is needed; Marty Cagan’s model at SVPG suggests the research threshold model and suggests that high-risk decisions necessitate greater evidence. Prevent and counter confirmation bias with techniques like “post-mortems” and structured devil’s advocacy roles in the decision-making process that have been empirically linked to better team decision quality in Harvard Business School research. Encourage learning from both the things that succeed and the things that don’t work by conducting regular retrospectives that focus on the quality of research, not just its outcomes.
These organizational capabilities elevate SaaS research from a benign activity to a fundamental competitive advantage.
When should you involve specialized researchers?
As your SaaS business scales think of:
In-house specialists: Research-focused roles become more strategic opportunities as your SaaS organization matures. In defending such user progress over time, dedicated user researchers treat their jobs as science — as companies like Intercom and Spotify have shown, specialized researchers can catch things product managers miss as they learn through their applied research training. Product metrics data analysts turn raw usage data into meaningful patterns— Market researchers monitoring the competitive landscape offer insights for strategy beyond the voice of the customer; Amazon was the first company I heard of maintaining distinct competitive intelligence teams to track movement across contiguous markets for encroachment opportunities.
External specialists: Engaging experts outside the organization can enhance the overall impact of research. Specialized research techniques that require a lot of experience, such as ethnographic research or large-scale quantitative studies, will be able to be accessed through specialized research agencies, or firms like AnswerLab and User Interviews that will perform specialized research on demand. Industry data from market analysis firms facilitates proprietary datasets and analyses that are impossible to generate in-house; market sizing and trend analysis are well-backed by methodologies honed over decades from Gartner, Forrester, and IDC.
How do you ensure research drives action?
What makes research insights actionable?
Acting with intention is the final destination of SaaS research. To achieve this:
Link Insights to specific choices:
- Connect research directly to pending product decisions
- Findings mapped to roadmap items
- Test-through on novel leading actions, not just observations
Assessment of impact wherever feasible:
- Work out a potential revenue impact
- Improvements to retention of projects
- Compute cost savings or efficiency gains
Write concise, clear deliverables:
- Tailor research summaries to varied audiences
- Report in detail and then give an executive summary
- Build repositories of commonly used insights available to teams
These practices ensure that your SaaS research leads to real business impact.
Conclusion: Building a balanced SaaS research practice
“Talk to customers” is still good advice—but just the start of effective SaaS research. The insights you gain from expanding your research methodologies provide a full view of your market and opportunities.
The best SaaS companies strike the right balance of:
- Qualitative and Quantitative Approaches
- Up-to-date vet on customer insights and wider market awareness
- What consumers tell you they do versus what they do
- Immediate data and long-term trends
This more balanced approach to SaaS research lays the groundwork for product decisions that both respond to customers at the moment and anticipate their next-step needs in ways that create long-term competitive differentiators.
Remember the key point: In SaaS, the companies that know their markets best are usually the ones that win them. Make your comprehensive of research now and secure your future.
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